Veriscope AI-assisted AML investigations
I redesigned evidence review for AML investigations. The share of investigations returned for rework fell by 20%, while claim-level AI explanations helped users detect more AI errors in a controlled study.
The product name and branding elements have been changed to maintain confidentiality. The original product solutions and the results of their implementation are shown.
01
Overview
Project info
AML investigators turn fragmented alerts and records into decisions that must withstand quality review. I designed the queue, evidence comparison and claim-level AI explanations so investigators could resolve conflicts and request missing records before submitting a case. After implementation, fewer investigations were returned for rework, and users detected more AI errors in a controlled study.
02
The challenge
A risk score alone did not show which evidence mattered, where sources disagreed or when automation should stop. The product had to reduce evidence assembly while keeping every outcome with the investigator.
- Traceability
- Every decisive AI claim needed a visible path to a transaction, KYC record, registry entry or document.
- Decision authority
- AI could rank cases, summarise evidence and flag conflicts. Only an investigator could clear or escalate a case.
- Safe failure
- Low confidence, missing data and conflicting sources had to block finalisation and offer a specific recovery action.
03
Why AI
The investigator must compare many records, connect entities and explain a suspicious pattern. AI is useful for preparing that review when its claims remain inspectable.
Cross-source synthesis
The model links transactions, entities, KYC data, registry entries and documents around one case.
Pattern detection
It surfaces transaction paths and ownership inconsistencies that deserve investigator attention.
Review preparation
It drafts sourced claims and identifies missing evidence before the human decision.
04
AI system model
The system converts fragmented records into reviewable claims and records the human outcome.
- 01
Collect
Bring the alert, transactions, KYC data, registry entries and documents into one case.
- 02
Resolve
Match entities and infer relevant risk patterns while retaining source identity.
- 03
Explain
Produce claims with confidence, provenance and missing-evidence status.
- 04
Review
Let the investigator compare sources and resolve ambiguity before deciding.
- 05
Record
Write the confirmed decision and its evidence history to an immutable log.
05
Key decisions
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Define the evidence contract
I defined the minimum source information for a claim: origin, timestamp, controller, confidence and missing evidence. This contract shaped both the interface and the evaluation.
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Keep judgement with the investigator
I kept clear and escalate as human actions. The model prepares a recommendation, while unresolved conflicts pause it until the investigator compares the records and addresses the missing evidence.
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Evaluate investigation quality
The evaluation covers investigations returned for rework and users' assessment of AI recommendations. A controlled study measured detection of AI errors; recognition of correct recommendations was tracked alongside it.
Reviewers need a source for every claim that changes the decision.
06
The solution
Evidence-led investigation queue
Risk, owner and SLA appear in the first viewport. Filters support rapid triage, while the active case and its workflow remain visible in a separate navigation rail.
- Observation
- Analysts needed to find urgent cases before opening each alert.
- Decision
- Expose risk drivers, ownership and time pressure in the queue.
- Effect
- Investigators can review priority, ownership and deadlines directly from the queue.
Transaction path with decisive evidence
The evidence graph keeps entities and transactions beside the records that support the suspicious path. Each source shows its origin, date and conflict state.
- Observation
- A detached score hid the route that made the activity suspicious.
- Decision
- Place the transaction path and decisive source rail on the same screen.
- Effect
- Investigators can inspect the path without reconstructing it across tools.
Claim-level AI rationale
The recommendation is split into claims. Confidence, source coverage and missing evidence sit next to each conclusion, so the investigator can review the model one statement at a time.
- Observation
- A single confidence score did not reveal which conclusion needed attention.
- Decision
- Break the rationale into sourced claims with local confidence and coverage.
- Effect
- In a controlled study, users detected 73% of AI errors, up from 65%, a 12.3% relative increase. Recognition of correct recommendations remained at 90%.
Conflict recovery before decision
When ownership sources disagree, Veriscope pauses the recommendation. The investigator compares both records, sees the missing tie-breaker and requests the exact evidence needed to continue.
- Observation
- Conflicting records and missing sources could remain unresolved when an investigation reached quality review.
- Decision
- Compare the conflicting records, identify the missing source and offer a targeted evidence request before quality review.
- Effect
- After implementation, the share of investigations returned for rework fell from 30% to 24%, a 20% relative reduction.
07
Human oversight and recovery
Failure and recovery states
Low confidence
- When
- A decisive claim falls below the review threshold.
- Product response
- The recommendation pauses and the weakest claim is brought into focus.
Conflicting sources
- When
- Two authoritative records disagree on a material fact.
- Product response
- Both values are shown side by side and finalisation is blocked.
Missing or stale data
- When
- Required evidence is absent or outside its accepted freshness window.
- Product response
- The interface identifies the missing record and offers a targeted evidence request.
Human override
- When
- The investigator reaches a different conclusion after reviewing the sources.
- Product response
- The original model state and the confirmed human rationale remain in the audit history.
Safety and review controls
Visible provenance
Every decisive claim links to named evidence with a timestamp and controller.
Local confidence
Confidence is attached to the claim it describes instead of being reduced to one case score.
Blocked ambiguity
An unresolved conflict prevents clear or escalate actions from being recorded.
Immutable history
Model states, source changes and human actions remain available for audit.
08
AI evaluation
The results cover two aspects of the design: investigation quality at handoff and users' ability to assess AI recommendations. Returns for rework measure the workflow outcome; the controlled study measures human detection of AI errors.
Evaluation methods
Returns for rework
The share of investigations returned for rework fell from 30% to 24%, a 20% relative reduction.
Controlled study of AI error detection
Users' detection of incorrect AI recommendations increased from 65% to 73%. The measure describes how people assess recommendations, rather than the model's accuracy.
Recognition of correct recommendations
Recognition of correct AI recommendations was 90% before and after the change. This measure is reported separately from the detection of incorrect recommendations.
09
Results
After implementation, the share of investigations returned for rework fell from 30% to 24%. In a controlled study, users' detection of AI errors increased from 65% to 73%, while recognition of correct recommendations was 90% before and after the change. These results describe investigation quality and human review of AI recommendations.
10
Reflection
I focused the design on comparing evidence and resolving gaps before quality review. The results connect those choices to fewer returned investigations and better detection of incorrect AI recommendations. Tracking correct recommendations alongside errors keeps the evaluation focused on informed judgement, with the final decision remaining with the investigator.
11
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